DCT-Based Denoising in Multichannel Imaging With Reference

Abramov, Sergey; Abramova, Victoriya; Astola, Jaakko; Егиазарян, Карен

doi:10.1615/telecomradeng.v75.i13.30

Cited by 3 publications

(8 citation statements)

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“…Usually, if a given filter is more efficient in component-wise (single-channel) denoising, its use is also beneficial in the considered denoising with a reference [30]. It is also worth stressing that optimal (recommended) parameters of thresholds applied in DCT coefficient thresholding have been determined in the literature [24,30,31]. These thresholds differ from those usually recommended for the cases in which these filters are employed for noise removal in single channel images.…”

Section: Ijmentioning

confidence: 99%

“…We start analyzing the performance of methods of image filtering with reference(s) for simulated data [24,30,31]. In our simulations, four test images typical for remote sensing, presented in Figure 4 and denoted as FR01, FR02, FR03, and FR04, and two high quality component images denoted RS1 and RS2 of AVIRIS hypercube of data were used.…”

Section: Performance Criteriamentioning

confidence: 99%

“…Important items here are to find a proper reference and to make it as "close" to the noisy image as possible (e.g., by appropriate nonlinear transformation [31]). The approach [24,30,31] allows using both DCT [15] and BM3D [32] filters as well as to easily cope with signal-dependent noise in the component image to be denoised by applying a proper variance stabilizing transform (VST) to it before filtering.…”

Section: Introductionmentioning

confidence: 99%

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Enhancement of Component Images of Multispectral Data by Denoising with Reference

et al. 2019

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Multispectral remote sensing data may contain component images that are heavily corrupted by noise and the pre-filtering (denoising) procedure is often applied to enhance these component images. To do this, one can use reference images—component images having relatively high quality and that are similar to the image subject to pre-filtering. Here, we study the following problems: how to select component images that can be used as references (e.g., for the Sentinel multispectral remote sensing data) and how to perform the actual denoising. We demonstrate that component images of the same resolution as well as component images of a better resolution can be used as references. To provide high efficiency of denoising, reference images have to be transformed using linear or nonlinear transformations. This paper proposes a practical approach to doing this. Examples of denoising tests and real-life images demonstrate high efficiency of the proposed approach.

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Section: Ijmentioning

confidence: 99%

Section: Performance Criteriamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Enhancement of Component Images of Multispectral Data by Denoising with Reference

et al. 2019

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“…There was a discussion concerning is it worth keeping junk channels for further processing and analysis [4,21]. Currently many researchers consider that it is worth keeping them for further consideration under condition that images in 'junk channels' are pre-filtered with high efficiency [4,[22][23][24]. A question is how such filtering can be done?…”

Section: Introductionmentioning

confidence: 99%

“…Recently, the use of non-local based approaches to denoising multichannel images has become popular [2,24,29]. The main progress and benefits result from the fact that similar patches that can be used in collaborative denoising can be found not only in a given component image, but also in other component images.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of Component Images of Multispectral Data by Denoising with Reference

Abramov¹,

Uss²,

Vozel³

et al. 2019

Preprint

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Multispectral remote sensing data may contain component images which are heavily corrupted by noise and pre-filtering (denoising) procedure is often applied to enhance these component images. To do this, one can use reference images – component images having relatively high quality and which are similar to the image subject to pre-filtering. Here we study the following problems: how to select component images that can be used as references (e.g., for the Sentinel multispectral remote sensing data) and how to perform the actual denoising. We demonstrate that component images of the same resolution as well as component images of a better resolution can be used as references. Examples of denoising of real-life images demonstrate high efficiency of the proposed approach.

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Compressive Sensing Inspired Multivariate Median

Stanković

Daković

2018

Circuits Syst Signal Process

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DCT-Based Denoising in Multichannel Imaging With Reference

Cited by 3 publications

References 0 publications

Enhancement of Component Images of Multispectral Data by Denoising with Reference

Enhancement of Component Images of Multispectral Data by Denoising with Reference

Enhancement of Component Images of Multispectral Data by Denoising with Reference

Compressive Sensing Inspired Multivariate Median

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